Analysis methods of treatment incoherent radar images in remote sensing system based on filter speckle – noise

Stanovoy, A.; Yanushkevich, V.

Please use this identifier to cite or link to this item: https://elib.psu.by/handle/123456789/36937

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DC Field	Value	Language
dc.contributor.author	Stanovoy, A.	ru_RU
dc.contributor.author	Yanushkevich, V.	ru_RU
dc.date.accessioned	2023-01-09T08:35:25Z	-
dc.date.available	2023-01-09T08:35:25Z	-
dc.date.issued	2017
dc.identifier.citation	Stanovoy, A. Analysis methods of treatment incoherent radar images in remote sensing system based on filter speckle – noise / A. Stanovoy, V. Yanushkevich // European and National Dimension in Research = Европейский и национальный контексты в научных исследованиях : electronic collected materials of IX Junior Researchers' Conference, Novopolotsk, April 26-27, 2017 : in 3 parts / Ministry of Education of Belarus, Polotsk State University ; ed. Dr. Dzmitry Lazouski [et al.]. - Novopolotsk : PSU, 2017. - Part 3 : Technology. - P. 333-335.	ru_RU
dc.identifier.uri	https://elib.psu.by/handle/123456789/36937	-
dc.description.abstract	The analysis of non-coherent processing of radar imaging methods. Availability speckle - noise leads to deterioration decryption radar image (RI), radiometric resolution and accuracy of the measurement object components radar portrait, as well as increased requirements for the data channel and means for displaying information. The main method of reducing speckle - noise is filtering (linear, adaptive and combined methods) generated as a result of the synthesis of the amplitude or luminance RI. Home treatment of such filtering - incoherent accumulation.	ru_RU
dc.language.iso	en	ru_RU
dc.publisher	Полоцкий государственный университет	ru_RU
dc.title	Analysis methods of treatment incoherent radar images in remote sensing system based on filter speckle – noise	ru_RU
dc.type	Article	ru_RU
dc.citation.spage	333	ru_RU
dc.citation.epage	335	ru_RU
Appears in Collections:	European and National Dimension in Research. Technology. 2017

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